VADeaths
Rural Male Rural Female Urban Male Urban Female
50-54 11.7 8.7 15.4 8.4
55-59 18.1 11.7 24.3 13.6
60-64 26.9 20.3 37.0 19.3
65-69 41.0 30.9 54.6 35.1
70-74 66.0 54.3 71.1 50.0


barplot(
gd$`Rural Male`,
horiz = T,
col = "skyblue",
border = "red",
names.arg = c("50-54", "55-59", "60-64", "65-69", "70-74"),
main = "Death Rate among Rural Male in Virginia",
xlab = "Age Group",
ylab = "Rate"
)

Stacked Bar diagram

legend("topright", legend = rownames(VADeaths))
Error in (function (s, units = "user", cex = NULL, font = NULL, vfont = NULL, :
plot.new has not been called yet
Pie Chart

Pie Charts with Percentage
gd$piePercent <- round(100*gd$`Rural Male`/sum(gd$`Rural Male`))
pie(
gd$`Rural Male`,
labels = paste(gd$piePercent, "%"),
main = "% Deaths by Age groups for Rural Male",
col = rainbow(length(gd$`Rural Male`))
)
legend(
"topright",
c("50-54", "55-59", "60-64", "65-69", "70-74"),
cex = 0.8,
fill = rainbow(length(gd$`Rural Male`))
)

Air Passengers data

Histogram
gdcar <- as.data.frame(cars)
str(gdcar)
'data.frame': 50 obs. of 2 variables:
$ speed: num 4 4 7 7 8 9 10 10 10 11 ...
$ dist : num 2 10 4 22 16 10 18 26 34 17 ...
hist(gdcar$speed, col = "steelblue")

# add number of breaks
hist(gdcar$speed, col = "steelblue", breaks = 10)

dens <- density(cars$speed)
plot(dens, frame=FALSE, col="steelblue", main="Desnsity Plot of MPG")

Q-Q plot with Q-Q line
qqnorm(gdcar$speed)
qqline(gdcar$speed, col="red")

Scatterplot
- Speed = x-axis = independent variable
plot(gdcar$speed, gdcar$dist)

Boxplot: Easiest way to detect outliers
boxplot(mpg ~ cyl, data = mtcars)

boxplot(
mpg ~ cyl,
data = mtcars,
xlab = "Number of cylinders",
ylab = "Miles per Gallon",
main = "Mileage Data"
)

qqnorm(mtcars$mpg)
# Normal Q-Q line
qqline(mtcars$mpg, col="red")

str(mtcars)
'data.frame': 32 obs. of 11 variables:
$ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
$ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
$ disp: num 160 160 108 258 360 ...
$ hp : num 110 110 93 110 175 105 245 62 95 123 ...
$ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
$ wt : num 2.62 2.88 2.32 3.21 3.44 ...
$ qsec: num 16.5 17 18.6 19.4 17 ...
$ vs : num 0 0 1 1 0 1 0 1 1 1 ...
$ am : num 1 1 1 0 0 0 0 0 0 0 ...
$ gear: num 4 4 4 3 3 3 3 4 4 4 ...
$ carb: num 4 4 1 1 2 1 4 2 2 4 ...
df <- as.data.frame(mtcars)
f.cyl <- as.factor(df$cyl)
bpd <- table(df$cyl)
barplot(bpd)

l
[1] 5
Histogram and abline for mean of “mpg”
hist(df$mpg, col="steelblue", main="Histogram of MPG", xlab="MPG Categories")
abline(v=mean(df$mpg), lwd=3, lty=2)

qqnorm(mtcars$mpg)
qqline(mtcars$mpg, col="red")

hist(df$mpg, col="steelblue", main="Histogram of MPG", xlab="MPG Categories")
abline(v=mode(df$mpg), lwd=3, lty=2)
Warning: NAs introduced by coercion

mode_value <- as.numeric(names(sort(table(df), decreasing = TRUE)[1]))
Error in table(df) : attempt to make a table with >= 2^31 elements
Correlation
- linear - Pearson correlation cofficient
- non-linear - Spearman Rank correlation cofficient
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